McHale, I and Laycock, PJ 2006, 'Applications of a General Stable Law Regression Model' , Journal of Applied Statistics, 33 (10) , pp. 1075-1084.

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Abstract

In this paper we present a method for performing regression with stable disturbances. The method of maximum likelihood is used to estimate both distribution and regression parameters. Our approach utilises a numerical integration procedure to calculate the stable density, followed by sequential quadratic programming optimisation procedures to obtain estimates and standard errors. A theoretical justification for the use of stable law regression is given followed by two real world practical examples of the method. First, we fit the stable law multiple regression model to housing price data and examine how the results differ from normal linear regression. Second, we calculate the beta coefficients for 26 companies from the Financial Times Ordinary Shares Index.

Item Type:	Article
Uncontrolled Keywords:	Stable distribution; heavy-tails; extreme values; regression
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics Subjects outside of the University Themes
Schools:	Colleges and Schools > College of Business & Law > Salford Business School > Management Science and Statistics
Journal or Publication Title:	Journal of Applied Statistics
Publisher:	Taylor & Francis
Refereed:	Yes
ISSN:	0266-4763
Depositing User:	H Kenna
Date Deposited:	21 Aug 2007 14:05
Last Modified:	17 Apr 2013 13:17
URI:	http://usir.salford.ac.uk/id/eprint/270

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